The burning platform is familiar: Rapid technological advancements, shifting market dynamics, and a complex regulatory landscape are reshaping industries at a breakneck pace. To respond, employees will need new skills, mindsets, and ways of operating.
Yet, at a time when development is critical to organizational success, some companies are moving in the opposite direction. They are dissolving senior learning roles or pushing learning deeper into human resources, further separating learning leaders from where strategic decisions are being made. It’s a time of both tremendous opportunity and risk. For chief learning officers (CLOs), the stakes have never been higher.
Learning leaders have dreamed about delivering personalized development at scale for decades. The best learning functions have made the most of the tools available to become partners in strategy, experts in development, and fluent in technology. With the advent of AI, CLOs and their organizations are ready for the next evolution: a fundamental transformation in the way organizational learning is delivered.
The future of learning isn’t about adding more training on top of work; it’s about reimagining work itself as inherently developmental. In this new paradigm, learning is no longer a “go away and do it” activity; rather, it is a seamless, integrated part of the work experience. Work and development are merged, and daily activities are the most important catalyst for professional growth.
CLOs are uniquely positioned to lead this transformation because they have deep relationships with the business and an understanding of the capabilities employees need to execute an organization’s strategy. This knowledge enables CLOs to design systems in which employees don’t just execute tasks but also grow, adapt, and innovate in real time. The question is no longer how to prepare people for the future of work but how to make work the engine of their preparation.
This article explores the new mandate for CLOs: to create environments where work and learning merge, powered by technology, data, and a culture of continuous development. It also outlines the principles, tools, and leadership shifts required for CLOs to thrive in this new era.
Designing work to be inherently developmental
The CLO role is evolving from a provider of training programs to an architect of developmental ecosystems. Successful CLOs design work as a platform for continuous growth, enabling employees to learn, adapt, and innovate as part of their daily routines.
By meeting employees where they are, CLOs can create seamless, real-time opportunities for growth that feel natural and intuitive rather than disruptive. This approach transforms technology from a stand-alone enabler into an integral part of how people work, learn, and perform.
Take, for example, the transformation already happening in frontline call centers. In this high-pressure environment, employees are expected to deliver exceptional customer service while managing complex interactions in real time. Advanced technologies are now being deployed not only to assist agents in being more productive but also to coach them as they work.
Imagine a call center agent handling a customer inquiry. AI-powered tools analyze the conversation in real time, providing the agent with actionable insights—such as suggested responses, next-best actions, or relevant product information—right in the flow of the interaction. These tools also act as coaches, offering feedback on tone, empathy, and communication style immediately after the call. Over time, these microcoaching moments compound, helping employees build critical skills while delivering better outcomes for customers. This is a powerful example of how technology can embed learning into the flow of work, making development a natural and continuous part of the employee experience.
At one professional-services organization, where evaluation is a cornerstone of both culture and development, the process is rigorous and time intensive. While evaluators invest significant effort, very little of it has historically gone toward improving their own feedback and coaching skills. To make the process more efficient, the organization launched a new gen-AI-powered evaluation tool. Learning leaders played a critical role in shaping its design, balancing productivity goals with development opportunities for evaluators.
Early versions of the tool helped evaluators recognize bias, synthesize feedback in the language of the competency model, and surface feedback themes. Future versions will include agentic coaching support for new evaluators that will help them strengthen their judgment as they work. By embedding developmental support into this core workflow, the organization is transforming evaluation from a performance management task into a capability that builds more effective people leaders.
How CLOs can accelerate change
To be effective in an environment of rapid technological advancement and evolving workforce demands, CLOs can embrace three transformational changes: lead beyond the learning function, get serious about data, and align learning with strategic business outcomes.
Lead beyond the learning function
CLOs can step into a broader leadership role, creating cross-functional teams that bring together expertise from HR, operations, technology, analytics, and business units. These teams can focus on building a shared understanding of what skills are needed to close the gap between what the organization has and what it needs. CLOs can start by taking the following steps:
- Partnering on strategic workforce planning. Collaborate with the chief strategy officer to align workforce capabilities with long-term priorities, such as entering new markets or pivoting to new business models.
- Leveraging AI for skills intelligence. Work with the chief AI officer to map current capabilities, predict future needs, and personalize development at scale. For example, a global telecom company recently deployed AI-powered training and coaching tools that assessed employees’ skills and recommended personalized development pathways. The system helped employees upskill on new digital tools faster and gave leaders a dynamic view of workforce capabilities, enabling smarter deployment of talent. By combining skills intelligence with personalization, CLOs can ensure that development is both scalable and directly tied to strategic business outcomes.
- Shaping core technology with technologists and business leaders. Partner with technology and business leaders to influence the design, development, and deployment of the core tools and platforms employees use to do their work. By embedding development support into these systems, CLOs can ensure that technology not only drives productivity but also accelerates growth and adaptability.
Get serious about data
CLOs can rethink how they measure and track skill development in the flow of work. Traditional metrics such as learning hours, course completions, or satisfaction surveys no longer reflect the realities of a skills-driven, work-integrated approach. Instead, CLOs can build systems that provide real-time insights into how employees are developing capabilities as they work—and how interventions are or are not driving measurable progress. These systems include:
Tracking skills in the flow of work. Move beyond static assessments and focus on real-time data that shows how employees are building and applying skills in their daily tasks. For example, use real metrics such as project outcomes, performance data, and feedback loops to measure skill progression as it happens.
In fast-paced product teams at consumer software-as-a-service companies, sprint planning is a daily operating rhythm where work is scoped, prioritized, and assigned using tools like Jira or Linear. What if this routine became a developmental engine? A smart-coaching layer embedded in the task dashboard could surface real-time prompts such as, “Charlotte has led two recent experiments, let’s assign her to stakeholder comms to broaden her skill set,” or “Amos has shadowed data synthesis, it’s time to let him lead.” Each task is tagged to capabilities like prioritization or customer insight, and the system tracks how team members build skills over time.
Rather than relying solely on managers to identify stretch assignments ad hoc, the system acts as a coach by helping to identify growth patterns, plateaus, and where an employee needs practice. With the CLO co-owning the design, sprint planning becomes both a product delivery process and a leadership development tool.
Generating skills practice data. Create learning experiences that are designed to generate structured skills data, providing insights into an individual’s level of skillfulness and how it evolves through practice. By embedding opportunities for deliberate practice into workflows and tools, CLOs can capture meaningful data on how employees are applying and improving their skills in real time. This data becomes a critical input for understanding the effectiveness of interventions and identifying areas for further development.
Imagine an onboarding program for software engineers designed to build core judgment skills from day one. New hires complete short exercises that mimic real code changes, identifying issues such as unclear logic or not enough testing to ensure the program works correctly. An AI coach provides instant personalized feedback based on team standards.
But this skills data is also meaningful in aggregate. The system captures structured practice data—for example, how accurately new joiners spot issues, which areas improve over time, and how their review quality evolves. The data gives learning leaders and managers insight into ramp-up progress and creates a data-driven way to adjust onboarding over time to ensure it increases employee skills.
Building dynamic skills intelligence systems. Integrate data from across the organization (for example, learning platforms, performance reviews, project outcomes, and external labor market trends) to create a holistic view of workforce capabilities. This practice allows CLOs to identify critical skills, predict future needs, and ensure that employees are developing the right capabilities to meet business demands.
In most procurement functions, professionals regularly engage in complex vendor negotiations, balancing a complicated set of competing priorities. A core difference between good procurement professionals and great ones is their judgment, specifically how they read situations, decide when to push, when to concede, and how to read intentions. Outcomes get logged into sourcing platforms or contract management systems, but the richness and insight from the work of negotiating are often lost. Judgment is built over time, but often not in rapid, systemic, or replicable ways.
Adding a tech-enabled reflection engine to the procurement system could transform that moment of closing out the documentation into a development loop. After logging a negotiation, the system can prompt just-in-time reflection: “What was your hardest trade-off in this negotiation?” or “Where were you uncertain—and what will you watch for next time?”
Over time, the platform identifies patterns in how each professional approaches influence, risk tolerance, and decision-making. It offers anonymized peer comparisons (for example, “Others in similar deals took a firmer stance—want to see their approach?”), nudges for practice, and coaching prompts for managers.
By inserting a brief pause in existing workflows, an organization can create a new stream of developmental-insights data. With CLO involvement in the system design, judgment becomes something the organization intentionally builds.
Align learning with strategic business outcomes
The future of learning is not about abandoning structured programs; it’s about reimagining their role. Immersive learning programs, including classes and webinars, will remain essential for passing on culture and leadership. But for skill development, the real opportunity lies in the merged coexistence of work and learning. When work is developmental, employees can build and refine skills in real time while still directly contributing to business outcomes.
To unleash this potential, CLOs can shift their focus from tracking learning activities to measuring the organizational impact of skill building. The question is no longer, “How many people completed a course?” or “How many hours did someone spend learning?” but rather, “How is learning enabling the organization to adapt, innovate, and grow?” This requires a new set of metrics that reflects the agility and outcomes learning creates. These include:
- Skill progression in context. Are employees developing the capabilities required to solve real business challenges? This means tracking not just skill acquisition but also whether the skills are being applied to deliver results in dynamic, high-stakes environments.
- Organizational agility. How effectively is learning enabling the organization to respond to change? This means using skill visibility and rapid development to redeploy talent, pivot to new opportunities, and scale capabilities as demands shift.
- Tangible business outcomes. How is learning contributing to measurable results, such as faster time to market, improved customer satisfaction, or increased operational efficiency? CLOs can track these by linking skill-building efforts to performance indicators in business workflows, showing how development enables better outcomes.
By focusing on these metrics, CLOs can ensure that learning is not just an isolated activity but also a strategic tool for adaptability and growth. This approach transforms learning into a dynamic force that enables the organization to thrive in an environment of constant change (exhibit).
Pivoting the learning organization to shape the future of work
The learning and development function has long contributed to strategy execution—delivering at scale, leveraging technology, and applying human-centered design to create impact. Building on that foundation, CLOs now need to evolve their teams into architects of work design. This means using the expertise of the learning organization to shape how work itself is structured, ensuring it drives both business outcomes and human development. To that end, CLOs can consider taking the following steps.
Redirect the expertise of designers to redesign work
Learning designers have long excelled at creating experiences that build skills, foster engagement, and drive behavior change. Now, CLOs can redirect this expertise to influence how work itself is designed.
When building an AI agent to take over a specific task, learning designers can collaborate with technology teams to ensure that the agent is designed such that it reinforces critical cognitive development. This might include structuring interactions with the AI agent to encourage decision-making, problem-solving, or strategic thinking so employees increase their skills even as tasks are being automated.
Reimagine needs analysis as strategic workforce design
Traditional needs analysis focuses on identifying skill gaps and aligning learning initiatives with business needs. CLOs can elevate this capability by embedding learning teams into strategic workforce planning to shape how work is designed and executed.
Instead of identifying skills employees lack, learning teams can partner with business leaders to analyze how roles, workflows, and even organizational structures need to evolve to meet future challenges. This might include mapping out the capabilities required for emerging technologies, such as gen AI, and designing roles or tasks that simultaneously drive business outcomes and foster employee growth. By taking a proactive, systems-level approach, learning teams can ensure that workforce design is not reactive but anticipatory, enabling the organization to stay ahead of disruptions.
Redefine metrics to track workforce capability in real time
Traditional learning metrics focus on program completion rates and post-training feedback. CLOs can redeploy this capability to create dynamic, real-time dashboards that measure skill development and proficiency directly within the flow of work.
Learning teams can integrate data from workflow systems, performance tools, and peer feedback to track how employees apply and improve their skills on the job, identify emerging gaps, and forecast future capability needs. These insights could then be used to support cohorts with targeted role redesigns, optimize team structures, and ensure the organization remains agile in the face of change. By redefining metrics in this way, learning organizations become indispensable partners in shaping a future-ready workforce.
The traditional model of treating learning as a separate, episodic activity no longer meets the demands of scale, speed, and personalization. The convergence of work and learning is not just a shift in how organizations operate—it’s a redefinition of what it means to grow, adapt, and thrive. For CLOs, this moment calls for bold action to move beyond traditional boundaries and design systems that make work itself the catalyst for organizational evolution.